Understanding the Confusion Matrix
In machine learning, we can build various models, ranging from simple linear regression to complex neural network models. These models can be applied to different fields, such as image recognition, language generation, and financial forecasting. Each model has its advantages and applicable scenarios. For example, linear regression is suitable for fitting and analyzing datasets with target variables, while deep learning can handle complex nonlinear datasets. However, how do we assess the quality of a model? Simply relying on accuracy may not be sufficient to evaluate model performance, especially in cases with imbalanced class distributions. Therefore, we use **Confusion Matrix** to analyze the performance of classification models.
